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Designing and Evaluating Language Corpora: A Practical Framework for Corpus Representativeness

Autor Jesse Egbert, Douglas Biber, Bethany Gray
en Limba Engleză Paperback – 13 apr 2022
Corpora are ubiquitous in linguistic research, yet to date, there has been no consensus on how to conceptualize corpus representativeness and collect corpus samples. This pioneering book bridges this gap by introducing a conceptual and methodological framework for corpus design and representativeness. Written by experts in the field, it shows how corpora can be designed and built in a way that is both optimally suited to specific research agendas, and adequately representative of the types of language use in question. It considers questions such as 'what types of texts should be included in the corpus?', and 'how many texts are required?' – highlighting that the degree of representativeness rests on the dual pillars of domain considerations and distribution considerations. The authors introduce, explain, and illustrate all aspects of this corpus representativeness framework in a step-by-step fashion, using examples and activities to help readers develop practical skills in corpus design and evaluation.
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Specificații

ISBN-13: 9781316605882
ISBN-10: 1316605884
Pagini: 250
Dimensiuni: 152 x 228 x 15 mm
Greutate: 0.4 kg
Ediția:Nouă
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom

Cuprins

1. Introduction; 2. Approaches to representativeness in previous corpus linguistic research; 3. Corpus representativeness: a conceptual and methodological framework; 4. Domain considerations; 5. Distribution considerations; 6. The influence of domain and distribution considerations on corpus representativeness – bringing it all together; 7. Corpus design and representativeness in practice; Glossary; Appendix A. Example articles documenting existing corpora; Appendix B. Survey of corpus design and compilation practices.

Descriere

This volume introduces a new framework for conceptualizing and achieving corpus representativeness in a rigorous, yet practical way.